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  • MDPI AG  (11)
  • 1
    Online Resource
    Online Resource
    Grouting Process Simulation Based on 3D Fracture Network Considering Fluid–Structure Interaction
    MDPI AG ; 2019
    In:  Applied Sciences Vol. 9, No. 4 ( 2019-02-15), p. 667-
    Details
    In: Applied Sciences, MDPI AG, Vol. 9, No. 4 ( 2019-02-15), p. 667-
    Abstract: Grouting has always been the main engineering measure of ground improvement and foundation remediation of hydraulic structures. Due to complex geological conditions and the interactions between the grout and the fractured rock mass, which poses a serious challenge to the grouting diffusion mechanism analysis, fracture grouting has been a research hotspot for a long time. In order to throw light on the grout diffusion process in the fractured rock mass and the influence of grout on the fracture network, and to achieve more realistic grouting numerical simulation, in this paper a grouting process simulation approach considering fluid–structure interaction is developed based on the 3D fractured network model. Firstly, the relationship between fracture apertures and trace lengths is used to obtain a more realistic value of fracture aperture; then a more reliable model is established; subsequently, based on the 3D fracture network model, different numerical models are established to calculate fluid dynamics (grout) and structure deformation (fractured rock mass), and the results are exchanged at the fluid–structure interface to realize the grouting process simulation using two-way fluid-structure interaction method. Finally, the approach is applied to analyze the grouting performance of a hydropower station X, and the results show that the grouting simulation considering fluid–structure interaction are more realistic and can simultaneously reveal the diffusion of grout and the deformation of fracture, which indicates that it is necessary to consider the effect of fluid–structure interaction in grouting simulation. The results can provide more valuable information for grouting construction.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app9040667
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2704225-X
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  • 2
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    Online Resource
    Factors Influencing O3 Concentration in Traffic and Urban Environments: A Case Study of Guangzhou City
    MDPI AG ; 2022
    In:  International Journal of Environmental Research and Public Health Vol. 19, No. 19 ( 2022-10-10), p. 12961-
    Details
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 19, No. 19 ( 2022-10-10), p. 12961-
    Abstract: Ozone (O3) pollution is a serious issue in China, posing a significant threat to people’s health. Traffic emissions are the main pollutant source in urban areas. NOX and volatile organic compounds (VOCs) from traffic emissions are the main precursors of O3. Thus, it is crucial to investigate the relationship between traffic conditions and O3 pollution. This study focused on the potential relationship between O3 concentration and traffic conditions at a roadside and urban background in Guangzhou, one of the largest cities in China. The results demonstrated that no significant difference in the O3 concentration was observed between roadside and urban background environments. However, the O3 concentration was 2 to 3 times higher on sunny days (above 90 μg/m3) than on cloudy days due to meteorological conditions. The results confirmed that limiting traffic emissions may increase O3 concentrations in Guangzhou. Therefore, the focus should be on industrial, energy, and transportation emission mitigation and the influence of meteorological conditions to minimize O3 pollution. The results in this study provide some theoretical basis for mitigation emission policies in China.
    Type of Medium: Online Resource
    ISSN: 1660-4601
    URL: Article
    DOI: 10.3390/ijerph191912961
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2175195-X
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  • 3
    Online Resource
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    Sub-Block Urban Function Recognition with the Integration of Multi-Source Data
    MDPI AG ; 2022
    In:  Sensors Vol. 22, No. 20 ( 2022-10-16), p. 7862-
    Details
    In: Sensors, MDPI AG, Vol. 22, No. 20 ( 2022-10-16), p. 7862-
    Abstract: The recognition of urban functional areas (UFAs) is of great significance for the understanding of urban structures and urban planning. Due to the limitation of data sources, early research was characterized by problems such as singular data, incomplete results, and inadequate consideration of the socioeconomic environment. The development of multi-source big data brings new opportunities for dynamic recognition of UFAs. In this study, a sub-block function recognition framework that integrates multi-feature information from building footprints, point-of-interest (POI) data, and Landsat images is proposed to classify UFAs at the sub-block level using a random forest model. The recognition accuracies of single- and mixed-function areas in the core urban area of Guangzhou, China, obtained by this framework are found to be significantly higher than those of other methods. The overall accuracy (OA) of single-function areas is 82%, which is 8–36% higher than that of other models. The research conclusions show that the introduction of the three-dimensional (3D) features of buildings and finer land cover features can improve the recognition accuracy of UFAs. The proposed method that uses open access data and achieves comprehensive results provides a more practical solution for the recognition of UFAs.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s22207862
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2052857-7
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  • 4
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    A Mathematical Kinetic Model and Network Analysis for Multicomponent Dissolution Relationships during the Extraction of Natural Products
    MDPI AG ; 2022
    In:  Processes Vol. 10, No. 8 ( 2022-07-27), p. 1470-
    Details
    In: Processes, MDPI AG, Vol. 10, No. 8 ( 2022-07-27), p. 1470-
    Abstract: Traditional Chinese Medicine (TCM) has a long history and typical ethnic traits. Astragalus and Angelica are used in a natural product called a buyang huanwu decoctionand are considered to function as both food and medicine; such products are called a “homology of medicine and food”. In this study, we examined the complex extraction kinetics that occur during the preparation of the natural product BYHWD. Mathematical tools, including the Laplace transformation and Fick’s law, were used to set up kinetic equations for different components in a model of the decoction. We selected the five most important bioactive ingredients of the BYHWD to find the most important speed control component. The intensity and capacity process parameters of the model were determined. A kinetic model was used to quantitatively analyze the dissolution restriction mechanism among the major components. Further, a component–effect network relationship was established to study the interactions of different components during extraction, considering the integrative effect of TCM compositions. Finally, using network pharmacology, certain network parameters were determined through topological analysis. The results indicate that Astragaloside IV exerts the strongest control over the dissolution rates of other components. The BYHWD has a short average path and a high clustering coefficient. The theoretical and experimental results can be used to quantitatively simulate and optimize TCM extraction processes.
    Type of Medium: Online Resource
    ISSN: 2227-9717
    URL: Article
    DOI: 10.3390/pr10081470
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2720994-5
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  • 5
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    Online Resource
    Implementation of a Single Quadrupole Mass Spectrometer for Fingerprint Analysis: Venenum bufonis as a Case Study
    MDPI AG ; 2018
    In:  Molecules Vol. 23, No. 11 ( 2018-11-19), p. 3020-
    Details
    In: Molecules, MDPI AG, Vol. 23, No. 11 ( 2018-11-19), p. 3020-
    Abstract: The mass spectrometry (MS) has been widely used for profiling chemical components of traditional Chinese medicine (TCM). However, there are few studies reporting quality control of TCM based on mass spectrometry fingerprint (MSF) due to its complicated operation and high cost. The aim of this study was to extend the application of MSF for quality evaluation of TCM. In this study, an MSF based on single quadrupole mass spectrometry method was established, and was successfully used for the quality control of Venenum bufonis (VB), a famous TCM which was used clinically for cancer treatment in China. The results showed that the superiority of MSF for more chemical information exposure and the finding of more potential chemical markers (eight versus four) compared with the traditional photo-diode array (a kind of ultra violet detector, PDA). Besides, the performance of MSF was also validated by similarity and principle component analysis (PCA) of MS data acquired on two other mass spectrometry (low-resolution, triple quadrupole, QQQ, and high-resolution, quadruple time-of-flight, Q-TOF), showing high consistency with QQQ and Q-TOF, but robustness with few parameters’ settings. Based on our study, MSF could be widely applied for the quality control of TCM.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    URL: Article
    DOI: 10.3390/molecules23113020
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2008644-1
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  • 6
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    Intergenerational Support and Second-Child Fertility Intention in the Chinese Sandwich Generation: The Parallel Mediation Model of Double Burnout
    MDPI AG ; 2023
    In:  Behavioral Sciences Vol. 13, No. 3 ( 2023-03-14), p. 256-
    Details
    In: Behavioral Sciences, MDPI AG, Vol. 13, No. 3 ( 2023-03-14), p. 256-
    Abstract: In 2013, the Chinese government implemented a two-child policy to increase the country’s fertility rate. However, the persistently low rates necessitated other measures to boost fertility. This study empirically investigated the association between intergenerational support and second-child fertility intention in the Chinese sandwich generation and demonstrated the mediating role of parental burnout and burnout in caring for grandparents. Survey data collected at Time 1, Time 2, and Time 3 included 2939 participants from different regions of China. Before analyzing the data, coarsened exact matching and propensity score matching was conducted to reduce sampling bias. Regression analysis results indicated that intergenerational support has a significant total positive effect on second-child fertility intention. Furthermore, mediation path analysis revealed that parental burnout and burnout in caring for grandparents play significant but opposite directional mediating roles in the association between intergenerational support and second-child fertility intention. Sensitivity analysis using different calipers yielded similar results. These results indicated that second-child fertility intention can be increased among the Chinese sandwich generation with intergenerational support, by mitigating parental burnout. However, intergenerational support did not alleviate burnout in caring for grandparents in the sandwich generation; therefore, formal older adult care policies are required to help the sandwich generation experience lower burnout, while receiving intergenerational support.
    Type of Medium: Online Resource
    ISSN: 2076-328X
    URL: Article
    DOI: 10.3390/bs13030256
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2651997-5
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  • 7
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    A Comparison of Machine Learning and Empirical Approaches for Deriving Bathymetry from Multispectral Imagery
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 2 ( 2023-01-09), p. 393-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 2 ( 2023-01-09), p. 393-
    Abstract: Knowledge of the precise water depth in shallow areas of the ocean is of great significance to the safe navigation of ships and hydrographic surveying. Compared with traditional bathymetry, satellite remote sensing for water depth determination makes it possible to cover large areas by dynamic observation. In this paper, we conducted an optically shallow water bathymetric inversion study using a Stumpf empirical model, random forest model, neural network model, and support vector machine model based on Sentinel-2 satellite images and Ganquan Dao measured bathymetry data. We compared and analyzed the inversion results based on the empirical model and different machine learning models. The results show that the Stumpf empirical and machine learning models are capable of inverting optically shallow water depth. Moreover, the machine learning models had better fitting ability than the Stumpf empirical model with a sufficient number of samples, especially when the water depth was greater than 15 m. In addition, the random forest model had the highest overall accuracy among these models, with a root mean square error (RMSE) of 1.41 m and a regression coefficient (R2) of 0.96 for the test data.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15020393
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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  • 8
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    Thermodynamic Behavior of As, Pb, and As during the Vacuum Carbothermal Reduction of Copper Anode Slime
    MDPI AG ; 2023
    In:  Applied Sciences Vol. 13, No. 10 ( 2023-05-10), p. 5878-
    Details
    In: Applied Sciences, MDPI AG, Vol. 13, No. 10 ( 2023-05-10), p. 5878-
    Abstract: The use of copper anode slime (CAS) for the removal of lead, bismuth, and arsenic is the key to recovering precious metals. In this paper, vacuum differential gravimetry experiments combined with thermodynamic equilibrium calculations reveal the effects of the temperature, system pressure, and carbon concentration on the interactions among Pb, Bi, and As during reduction. The carbon content is a direct factor limiting the reduction reactions of sulfate and arsenate phases, and affects the presence of arsenate reduction products. When the carbon content of the system is insufficient, As mainly exists as oxides in the reduction products, and the form of arsenic gradually converts to monomers with increasing carbon content. The reduction product Bi2S3 gradually converts to Bi and BiS as the temperature increases. The effect of temperature on arsenate is mainly related to the phase of the Pb- and Bi-containing reduction products. Moreover, vacuum differential gravimetry experiments were performed to verify the phase transformation of As, Pb, and Bi in CAS during vacuum carbon thermal reduction.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app13105878
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704225-X
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  • 9
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    Online Resource
    Entropy-Optimized Fault Diagnosis Based on Unsupervised Domain Adaptation
    MDPI AG ; 2023
    In:  Mathematics Vol. 11, No. 9 ( 2023-04-28), p. 2110-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 9 ( 2023-04-28), p. 2110-
    Abstract: In practice, the cross-domain transfer of data distribution and the sample imbalance of fault status are inevitable, but one or both are often ignored, which restricts the adaptability and classification accuracy of the generated fault diagnosis (FD) model. Accordingly, an entropy-optimized method is proposed in this paper based on an unsupervised domain-adaptive technique to enhance FD model training. For the training, pseudosamples and labels corresponding to the target samples are generated through data augmentation and self-training strategies to diminish the distribution discrepancy between the source and target domains. Meanwhile, an adaptive conditional entropy loss function is developed to improve the data quality of the semisupervised learning, with which reliable samples are generated for the training. According to the experiment results, compared with other state-of-the-art algorithms, our method can achieve significant accuracy improvement in rolling bearing FD. Typically, the accuracy improvement compared with the baseline Convolutional Neural Network (CNN) is achieved by over 13.23%.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11092110
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704244-3
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  • 10
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    Study on Prediction of Energy Conservation and Carbon Reduction in Universities Based on Exponential Smoothing
    MDPI AG ; 2022
    In:  Sustainability Vol. 14, No. 19 ( 2022-09-21), p. 11903-
    Details
    In: Sustainability, MDPI AG, Vol. 14, No. 19 ( 2022-09-21), p. 11903-
    Abstract: With the continuous development of China’s economy, the phenomenon of energy scarcity has become more and more prominent, for which China has put forward the strategic goal of carbon peak and carbon neutrality (double carbon target). As densely populated areas, the demand for energy is especially tight in universities. In combination with the work of “conservation-oriented colleges” carried out by the Ministry of Education, the accurate monthly electrical and water energy consumption of Kunming University of Science and Technology from 2018–2021 was counted, and the data were plotted into an energy consumption analysis chart to determine its compliance with the prediction range of the smoothing index prediction model. The corresponding smoothing indices were calculated by writing smoothing formulas through Excel, and, finally, the overall energy consumption indexes for 2022 and 2023 were successfully predicted using the exponential smoothing method. The errors between the real and forecasted values of electricity and water consumption in 2021 are reduced to 2.61% and 2.44%. The smoothing index predicts that the baseline discounted electricity energy consumption in 2022 is 5,423,658.235 kgce and in 2023 is 5,758,865.224 kgce; on the other hand, the baseline discounted water energy consumption in 2022 is predicted to be 632,654.321 kgce, while in 2023 it is predicted to be 652,321.238 kgce. The projected values can be used as an early warning line for the energy consumption index, and long-term management approaches and data support for energy conservation and carbon emission reduction can be effectively provided. The mentioned research provides an important reference for the proposal and implementation of efficient management measures, and provides strong theoretical technical support for the implementation of the carbon peak and neutrality in universities.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su141911903
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518383-7
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